<i>Dietitians’ Attitudes, Perceptions, and Usage Patterns</i> For Fresh-cut Fruit and Vegetables
Bibliographic record
Abstract
PURPOSE: New fresh-cut fruit and vegetable products are being developed worldwide. Nutrition educators' perceptions of these products were studied. METHODS: Professional dietitians in Nova Scotia were asked to complete a questionnaire on their use of fresh-cut produce. The questionnaire also elicited their attitudes and perceptions about the convenience, taste/quality, nutrition/health benefits, cost, and safety of fresh-cut fruit and vegetables. RESULTS: Sixty-three percent of respondents reported eating five to six servings of fruit and vegetables a day. This group most frequently consumed fresh-cut fruit as snacks or dessert, and vegetables in stir-fry dishes or salads or cooked with meals. In general, fresh-cut fruit and vegetables were perceived as convenient, safe, and nutritious. While approximately 50% of participants felt fresh-cut produce did not differ in taste from whole fresh produce, almost the same number considered whole fresh produce superior in taste. CONCLUSIONS: Dietitians have a generally positive perception of fresh-cut products; however, there is uncertainty about the nutritional value, cost/benefit, and use of the products. Dietitians require more information on the nutrient value of these products and on suggested alternative uses. Attention should be paid to developing fresh-cut products that have good sensory quality.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".